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Gaussian Processes for Machine Learning
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  • Title: Gaussian Processes for Machine Learning
  • Author(s): Carl Edward Rasmussen, Christopher K. I. Williams
  • Publisher: The MIT Press (November 23, 2005)
  • Permission: "The MIT Press have kindly agreed to allow us to make the book available on the web."
  • Hardcover/Paperback: 266 pages
  • eBook: PDF Files
  • Language: English
  • ISBN-10: 026218253X
  • ISBN-13: 978-0262182539
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Book Description

Gaussian Processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.

The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed.

The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

About the Authors
  • Carl Edward Rasmussen is a Lecturer at the Department of Engineering, University of Cambridge, and Adjunct Research Scientist at the Max Planck Institute for Biological Cybernetics, Tübingen.
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